Winter wheat yield per unit prediction method and system

Abstract

The invention relates to a winter wheat yield per unit area prediction method and system. The method includes calculating the cumulative average enhanced vegetation index of the to-be-measured area, the cumulative average enhanced vegetation index of the to-be-measured area and the technical yield of the to-be-measured area, establishing a municipal yield estimation model and a provincial yield estimation model by utilizing a multiple linear regression method of a least square method, and monitoring and predicting the unit yield of the winter wheat by utilizing the two models. The yield estimation model established by the invention has very high applicability to estimation of unit yield of municipal and provincial crops, the estimation result is accurate, dynamic yield prediction can be realized, and the growth condition and change of winter wheat can be known in time.

Description

technical field [0001] The invention relates to the technical field of agricultural engineering, in particular to a winter wheat yield prediction method and system. Background technique [0002] Remote sensing agricultural monitoring has the advantages of large-scale observation, high time resolution, and objective and reliable data. It saves time and cost compared with the traditional yield estimation method with heavy workload, high cost, and low efficiency, and provides a scientific and effective means for crop yield estimation. [0003] There are three methods for crop yield estimation using remote sensing: remote sensing statistical yield estimation model, dry matter-yield model and crop model simulation. [0004] The remote sensing statistical production estimation model estimates production by establishing the relationship expression between remote sensing variables and production. [0005] The dry matter-yield model first estimates the aboveground biomass of crops b...

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Problems solved by technology

[0007] Among the three methods, the first method is the simplest and requires the least data. The latter two methods are more mechanical, but require a large amount of input data and the operation is more complicated.

Although the construction of remote sensing yield estimation model using vegetation has achieved great success, there are still some problems, such as NDVI is easily saturated in high vegetation coverage areas, there is a lag in response to disasters (such as drought, pests and diseases), and the accuracy of yield estimation Difficult to guarantee, cannot well reflect the trend of increasing yield brought about by agricultural management and scientific and technological progress

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